Failure prediction in monitoring and control systems using Bayesian networks
2007
Predicting failure in complex systems, such as satellite network systems, is a challenging
problem. A satellite earth terminal contains many components, such high-powered amplifiers,
signal converters, modems, routers, and generators, any of which may cause system failure. The
ability to estimate accurately the probability of failure of any of these components, given the
current state of the system, may help reduce the cost of operation. Probabilistic graphical
models, in particular Bayesian networks, provide a consistent framework in which to address
problems containing uncertainty and complexity. Measurable nodes of the Bayesian network
correspond to states of measurable parameters in the system and unmeasurable nodes represent
failure of various components. Nodes for environmental factors are also included. A description
of Bayesian networks will be provided and a demonstration of inference on the Bayesian
network, such as the calculation of the marginal probability of failure nodes given measurements
and the maximum probability state of the system for failure diagnosis will be given.
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